Data Characteristics in This Category
Rational drug use data typically includes drug inserts, clinical guidelines, drug interactions, adverse reactions, contraindications, dosages, special population drug use guidance, and more. Data sources are diverse, including national drug administration databases, professional medical journals, pharmacopeias, clinical trial reports, and hospital pharmacy management systems. The update frequency is high. New drug approvals, expanded indications, adverse reaction reports, and revised clinical guidelines all trigger data updates. Some data, such as drug batch information, updates daily. Document structures are primarily semi-structured or unstructured text, such as PDF drug inserts, HTML clinical guidelines, and structured data like drug ingredient lists and interaction matrices. Fields and units are specialized, for example, dosage units (mg/kg, U/L), frequencies (tid, qd), treatment durations (days, weeks), and specific medical terminology and coding systems (e.g., ATC classification, ICD-10 disease codes).
Constraints on "Tool Calling and Plugins" Imposed by These Characteristics
High data update frequency requires tool calls to synchronize the latest information promptly. For example, if drug interaction or adverse reaction data is not updated in time, it can lead to inaccurate medication advice. The presence of semi-structured and unstructured documents necessitates effective information extraction and structuring before tool calling. This limits the effectiveness of simple text-matching plugins, requiring more sophisticated NLP and information extraction capabilities. Specialized fields and units, along with medical coding systems, demand that plugins understand and correctly parse this information. For instance, converting colloquial "three times a day" to the medical tid or mapping a drug's generic name to a standard code. Furthermore, because rational drug use involves life and health, there are extremely high requirements for the accuracy and recall rate of tool calls. Any misjudgment or omission can have severe consequences, thus placing higher demands on error handling and result validation mechanisms.
Configuration Settings
| Configuration Item | Suggested Value | Rationale for This Value |
|---|---|---|
externalApiTimeout | 60000 ms | Drug data API responses can be slow; sufficient time needs to be allocated. |
maxChunkSize | 800 characters | Drug inserts and guidelines contain large amounts of text; smaller segments facilitate semantic matching. |
topK | 10 entries | Initially retrieve more relevant items to improve accuracy, followed by re-ranking. |
embeddingModel | text-embedding-ada-002 or higher version | Improve vectorization accuracy for specialized medical terminology. |
fallbackStrategy | manual review | Critical medication advice involves safety; manual intervention is needed if automation fails. |
parsingTemplate | Calibrated by actual measurement | Optimize information extraction rules for different source document structures. |
Three Common Mistakes
- An external API call returns an empty response, and logs show an
HTTP 500error. The reason is that the drug code format in the request parameters does not meet the API interface requirements, causing the backend service to fail. - The medication dosage units returned by the plugin are inconsistent, for example, sometimes
mgand sometimesg. The symptom is that the recommended results cannot be directly used clinically. The reason is that units were not standardized during data parsing, or units from different sources were inconsistent. - The model fails to mention a crucial drug contraindication in its response. The reason is that the data returned by the tool call is insufficient, or the relevant contraindication information was identified as low relevance and filtered out in the original document.
How to Verify the Setup
- Test multiple typical medication scenarios (e.g., specific diseases, special populations) to verify if the drug information returned by the tool call is comprehensive and accurate, comparing it against the latest clinical guidelines.
- Validate the plugin's ability to extract information from drug inserts and clinical guidelines in various formats (e.g., PDF, HTML), checking if key fields such as dosage, adverse reactions, and contraindications are correctly parsed.
- Simulate abnormal situations, such as slow external API responses, erroneous data, or no data, to observe if FastGPT's error handling mechanism meets expectations, for example, whether it correctly triggers fallback strategies or provides clear prompts.
- Review tool call logs to confirm that the data structure and content of
request bodyandresponse bodymeet expectations, especially regarding the parsing and mapping of specialized medical fields.
Note: The values provided are common starting points and should be measured against your own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-21.